{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.vision import *\n",
    "import torch\n",
    "from models.resnet_cifar import *\n",
    "from utils import _get_accuracy\n",
    "torch.cuda.set_device(0)\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning, module=\"torch.nn.functional\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = untar_data(URLs.CIFAR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfms = get_transforms(do_flip=False)\n",
    "data = ImageDataBunch.from_folder(path, train = 'train', valid = 'test', bs = 64, size = 32, ds_tfms = tfms).normalize(cifar_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.show_batch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential\n",
       "======================================================================\n",
       "Layer (type)         Output Shape         Param #    Trainable \n",
       "======================================================================\n",
       "Conv2d               [64, 16, 16]         9,408      True      \n",
       "______________________________________________________________________\n",
       "BatchNorm2d          [64, 16, 16]         128        True      \n",
       "______________________________________________________________________\n",
       "ReLU                 [64, 16, 16]         0          False     \n",
       "______________________________________________________________________\n",
       "MaxPool2d            [64, 8, 8]           0          False     \n",
       "______________________________________________________________________\n",
       "Conv2d               [64, 8, 8]           36,864     True      \n",
       "______________________________________________________________________\n",
       "BatchNorm2d          [64, 8, 8]           128        True      \n",
       "______________________________________________________________________\n",
       "ReLU                 [64, 8, 8]           0          False     \n",
       "______________________________________________________________________\n",
       "Conv2d               [64, 8, 8]           36,864     True      \n",
       "______________________________________________________________________\n",
       "BatchNorm2d          [64, 8, 8]           128        True      \n",
       "______________________________________________________________________\n",
       "Conv2d               [64, 8, 8]           36,864     True      \n",
       "______________________________________________________________________\n",
       "BatchNorm2d          [64, 8, 8]           128        True      \n",
       "______________________________________________________________________\n",
       "ReLU                 [64, 8, 8]           0          False     \n",
       "______________________________________________________________________\n",
       "Conv2d               [64, 8, 8]           36,864     True      \n",
       "______________________________________________________________________\n",
       "BatchNorm2d          [64, 8, 8]           128        True      \n",
       "______________________________________________________________________\n",
       "Conv2d               [64, 8, 8]           36,864     True      \n",
       "______________________________________________________________________\n",
       "BatchNorm2d          [64, 8, 8]           128        True      \n",
       "______________________________________________________________________\n",
       "ReLU                 [64, 8, 8]           0          False     \n",
       "______________________________________________________________________\n",
       "Conv2d               [64, 8, 8]           36,864     True      \n",
       "______________________________________________________________________\n",
       "BatchNorm2d          [64, 8, 8]           128        True      \n",
       "______________________________________________________________________\n",
       "Conv2d               [128, 4, 4]          73,728     True      \n",
       "______________________________________________________________________\n",
       "BatchNorm2d          [128, 4, 4]          256        True      \n",
       "______________________________________________________________________\n",
       "ReLU                 [128, 4, 4]          0          False     \n",
       "______________________________________________________________________\n",
       "Conv2d               [128, 4, 4]          147,456    True      \n",
       "______________________________________________________________________\n",
       "BatchNorm2d          [128, 4, 4]          256        True      \n",
       "______________________________________________________________________\n",
       "Conv2d               [128, 4, 4]          8,192      True      \n",
       "______________________________________________________________________\n",
       "BatchNorm2d          [128, 4, 4]          256        True      \n",
       "______________________________________________________________________\n",
       "Conv2d               [128, 4, 4]          147,456    True      \n",
       "______________________________________________________________________\n",
       "BatchNorm2d          [128, 4, 4]          256        True      \n",
       "______________________________________________________________________\n",
       "ReLU                 [128, 4, 4]          0          False     \n",
       "______________________________________________________________________\n",
       "Conv2d               [128, 4, 4]          147,456    True      \n",
       "______________________________________________________________________\n",
       "BatchNorm2d          [128, 4, 4]          256        True      \n",
       "______________________________________________________________________\n",
       "Conv2d               [128, 4, 4]          147,456    True      \n",
       "______________________________________________________________________\n",
       "BatchNorm2d          [128, 4, 4]          256        True      \n",
       "______________________________________________________________________\n",
       "ReLU                 [128, 4, 4]          0          False     \n",
       "______________________________________________________________________\n",
       "Conv2d               [128, 4, 4]          147,456    True      \n",
       "______________________________________________________________________\n",
       "BatchNorm2d          [128, 4, 4]          256        True      \n",
       "______________________________________________________________________\n",
       "Conv2d               [128, 4, 4]          147,456    True      \n",
       "______________________________________________________________________\n",
       "BatchNorm2d          [128, 4, 4]          256        True      \n",
       "______________________________________________________________________\n",
       "ReLU                 [128, 4, 4]          0          False     \n",
       "______________________________________________________________________\n",
       "Conv2d               [128, 4, 4]          147,456    True      \n",
       "______________________________________________________________________\n",
       "BatchNorm2d          [128, 4, 4]          256        True      \n",
       "______________________________________________________________________\n",
       "Conv2d               [256, 2, 2]          294,912    True      \n",
       "______________________________________________________________________\n",
       "BatchNorm2d          [256, 2, 2]          512        True      \n",
       "______________________________________________________________________\n",
       "ReLU                 [256, 2, 2]          0          False     \n",
       "______________________________________________________________________\n",
       "Conv2d               [256, 2, 2]          589,824    True      \n",
       "______________________________________________________________________\n",
       "BatchNorm2d          [256, 2, 2]          512        True      \n",
       "______________________________________________________________________\n",
       "Conv2d               [256, 2, 2]          32,768     True      \n",
       "______________________________________________________________________\n",
       "BatchNorm2d          [256, 2, 2]          512        True      \n",
       "______________________________________________________________________\n",
       "Conv2d               [256, 2, 2]          589,824    True      \n",
       "______________________________________________________________________\n",
       "BatchNorm2d          [256, 2, 2]          512        True      \n",
       "______________________________________________________________________\n",
       "ReLU                 [256, 2, 2]          0          False     \n",
       "______________________________________________________________________\n",
       "Conv2d               [256, 2, 2]          589,824    True      \n",
       "______________________________________________________________________\n",
       "BatchNorm2d          [256, 2, 2]          512        True      \n",
       "______________________________________________________________________\n",
       "Conv2d               [256, 2, 2]          589,824    True      \n",
       "______________________________________________________________________\n",
       "BatchNorm2d          [256, 2, 2]          512        True      \n",
       "______________________________________________________________________\n",
       "ReLU                 [256, 2, 2]          0          False     \n",
       "______________________________________________________________________\n",
       "Conv2d               [256, 2, 2]          589,824    True      \n",
       "______________________________________________________________________\n",
       "BatchNorm2d          [256, 2, 2]          512        True      \n",
       "______________________________________________________________________\n",
       "Conv2d               [256, 2, 2]          589,824    True      \n",
       "______________________________________________________________________\n",
       "BatchNorm2d          [256, 2, 2]          512        True      \n",
       "______________________________________________________________________\n",
       "ReLU                 [256, 2, 2]          0          False     \n",
       "______________________________________________________________________\n",
       "Conv2d               [256, 2, 2]          589,824    True      \n",
       "______________________________________________________________________\n",
       "BatchNorm2d          [256, 2, 2]          512        True      \n",
       "______________________________________________________________________\n",
       "Conv2d               [256, 2, 2]          589,824    True      \n",
       "______________________________________________________________________\n",
       "BatchNorm2d          [256, 2, 2]          512        True      \n",
       "______________________________________________________________________\n",
       "ReLU                 [256, 2, 2]          0          False     \n",
       "______________________________________________________________________\n",
       "Conv2d               [256, 2, 2]          589,824    True      \n",
       "______________________________________________________________________\n",
       "BatchNorm2d          [256, 2, 2]          512        True      \n",
       "______________________________________________________________________\n",
       "Conv2d               [256, 2, 2]          589,824    True      \n",
       "______________________________________________________________________\n",
       "BatchNorm2d          [256, 2, 2]          512        True      \n",
       "______________________________________________________________________\n",
       "ReLU                 [256, 2, 2]          0          False     \n",
       "______________________________________________________________________\n",
       "Conv2d               [256, 2, 2]          589,824    True      \n",
       "______________________________________________________________________\n",
       "BatchNorm2d          [256, 2, 2]          512        True      \n",
       "______________________________________________________________________\n",
       "Conv2d               [512, 1, 1]          1,179,648  True      \n",
       "______________________________________________________________________\n",
       "BatchNorm2d          [512, 1, 1]          1,024      True      \n",
       "______________________________________________________________________\n",
       "ReLU                 [512, 1, 1]          0          False     \n",
       "______________________________________________________________________\n",
       "Conv2d               [512, 1, 1]          2,359,296  True      \n",
       "______________________________________________________________________\n",
       "BatchNorm2d          [512, 1, 1]          1,024      True      \n",
       "______________________________________________________________________\n",
       "Conv2d               [512, 1, 1]          131,072    True      \n",
       "______________________________________________________________________\n",
       "BatchNorm2d          [512, 1, 1]          1,024      True      \n",
       "______________________________________________________________________\n",
       "Conv2d               [512, 1, 1]          2,359,296  True      \n",
       "______________________________________________________________________\n",
       "BatchNorm2d          [512, 1, 1]          1,024      True      \n",
       "______________________________________________________________________\n",
       "ReLU                 [512, 1, 1]          0          False     \n",
       "______________________________________________________________________\n",
       "Conv2d               [512, 1, 1]          2,359,296  True      \n",
       "______________________________________________________________________\n",
       "BatchNorm2d          [512, 1, 1]          1,024      True      \n",
       "______________________________________________________________________\n",
       "Conv2d               [512, 1, 1]          2,359,296  True      \n",
       "______________________________________________________________________\n",
       "BatchNorm2d          [512, 1, 1]          1,024      True      \n",
       "______________________________________________________________________\n",
       "ReLU                 [512, 1, 1]          0          False     \n",
       "______________________________________________________________________\n",
       "Conv2d               [512, 1, 1]          2,359,296  True      \n",
       "______________________________________________________________________\n",
       "BatchNorm2d          [512, 1, 1]          1,024      True      \n",
       "______________________________________________________________________\n",
       "AdaptiveAvgPool2d    [512, 1, 1]          0          False     \n",
       "______________________________________________________________________\n",
       "AdaptiveMaxPool2d    [512, 1, 1]          0          False     \n",
       "______________________________________________________________________\n",
       "Flatten              [1024]               0          False     \n",
       "______________________________________________________________________\n",
       "BatchNorm1d          [1024]               2,048      True      \n",
       "______________________________________________________________________\n",
       "Dropout              [1024]               0          False     \n",
       "______________________________________________________________________\n",
       "Linear               [512]                524,800    True      \n",
       "______________________________________________________________________\n",
       "ReLU                 [512]                0          False     \n",
       "______________________________________________________________________\n",
       "BatchNorm1d          [512]                1,024      True      \n",
       "______________________________________________________________________\n",
       "Dropout              [512]                0          False     \n",
       "______________________________________________________________________\n",
       "Linear               [10]                 5,130      True      \n",
       "______________________________________________________________________\n",
       "\n",
       "Total params: 21,817,674\n",
       "Total trainable params: 21,817,674\n",
       "Total non-trainable params: 0\n",
       "Optimized with 'torch.optim.adam.Adam', betas=(0.9, 0.99)\n",
       "Using true weight decay as discussed in https://www.fast.ai/2018/07/02/adam-weight-decay/ \n",
       "Loss function : FlattenedLoss\n",
       "======================================================================\n",
       "Callbacks functions applied "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn = cnn_learner(data, models.resnet34, metrics = accuracy, pretrained = True)\n",
    "learn.unfreeze()\n",
    "learn.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07252320742637644"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_get_accuracy(data.train_dl, learn.model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.lr_find()\n",
    "learn.recorder.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
       "            progress {\n",
       "                /* gets rid of default border in Firefox and Opera. */\n",
       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "                background: #F44336;\n",
       "            }\n",
       "        </style>\n",
       "      <progress value='6' class='' max='20', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      30.00% [6/20 03:15<07:36]\n",
       "    </div>\n",
       "    \n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.586192</td>\n",
       "      <td>1.901789</td>\n",
       "      <td>0.368200</td>\n",
       "      <td>00:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.639834</td>\n",
       "      <td>1.177699</td>\n",
       "      <td>0.605000</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.078927</td>\n",
       "      <td>0.846079</td>\n",
       "      <td>0.726300</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.854948</td>\n",
       "      <td>0.688524</td>\n",
       "      <td>0.768800</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.733223</td>\n",
       "      <td>0.590017</td>\n",
       "      <td>0.805800</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.617758</td>\n",
       "      <td>0.578406</td>\n",
       "      <td>0.809200</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>\n",
       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
       "            progress {\n",
       "                /* gets rid of default border in Firefox and Opera. */\n",
       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "                background: #F44336;\n",
       "            }\n",
       "        </style>\n",
       "      <progress value='0' class='progress-bar-interrupted' max='781', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      Interrupted\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 0 with accuracy value: 0.36820000410079956.\n",
      "Better model found at epoch 1 with accuracy value: 0.6050000190734863.\n",
      "Better model found at epoch 2 with accuracy value: 0.7263000011444092.\n",
      "Better model found at epoch 3 with accuracy value: 0.7688000202178955.\n",
      "Better model found at epoch 4 with accuracy value: 0.8058000206947327.\n",
      "Better model found at epoch 5 with accuracy value: 0.8091999888420105.\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-b4247032023d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_one_cycle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_lr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1e-4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSaveModelCallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmonitor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'accuracy'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'max'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/pyt/lib/python3.7/site-packages/fastai/train.py\u001b[0m in \u001b[0;36mfit_one_cycle\u001b[0;34m(learn, cyc_len, max_lr, moms, div_factor, pct_start, final_div, wd, callbacks, tot_epochs, start_epoch)\u001b[0m\n\u001b[1;32m     21\u001b[0m     callbacks.append(OneCycleScheduler(learn, max_lr, moms=moms, div_factor=div_factor, pct_start=pct_start,\n\u001b[1;32m     22\u001b[0m                                        final_div=final_div, tot_epochs=tot_epochs, start_epoch=start_epoch))\n\u001b[0;32m---> 23\u001b[0;31m     \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcyc_len\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_lr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m def fit_fc(learn:Learner, tot_epochs:int=1, lr:float=defaults.lr,  moms:Tuple[float,float]=(0.95,0.85), start_pct:float=0.72,\n",
      "\u001b[0;32m~/anaconda3/envs/pyt/lib/python3.7/site-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, epochs, lr, wd, callbacks)\u001b[0m\n\u001b[1;32m    198\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mwd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m         \u001b[0mcallbacks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback_fns\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlistify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdefaults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextra_callback_fns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlistify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 200\u001b[0;31m         \u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    201\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcreate_opt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m->\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pyt/lib/python3.7/site-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(epochs, learn, callbacks, metrics)\u001b[0m\n\u001b[1;32m     99\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0myb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprogress_bar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_dl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpbar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m                 \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m                 \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloss_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    102\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pyt/lib/python3.7/site-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mloss_batch\u001b[0;34m(model, xb, yb, loss_func, opt, cb_handler)\u001b[0m\n\u001b[1;32m     24\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_listy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mxb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mxb\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_listy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0myb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0myb\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m     \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mxb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     27\u001b[0m     \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_loss_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pyt/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    539\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    540\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 541\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    542\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    543\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pyt/lib/python3.7/site-packages/torch/nn/modules/container.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m     90\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_modules\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m             \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     93\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pyt/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    539\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    540\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 541\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    542\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    543\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pyt/lib/python3.7/site-packages/torch/nn/modules/container.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m     90\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_modules\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m             \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     93\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pyt/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    539\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    540\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 541\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    542\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    543\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pyt/lib/python3.7/site-packages/torch/nn/modules/container.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m     90\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_modules\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m             \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     93\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pyt/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    539\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    540\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 541\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    542\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    543\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pyt/lib/python3.7/site-packages/torchvision/models/resnet.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     57\u001b[0m         \u001b[0midentity\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 59\u001b[0;31m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     60\u001b[0m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbn1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pyt/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    539\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    540\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 541\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    542\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    543\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pyt/lib/python3.7/site-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    343\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    344\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 345\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv2d_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    346\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    347\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mConv3d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_ConvNd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pyt/lib/python3.7/site-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36mconv2d_forward\u001b[0;34m(self, input, weight)\u001b[0m\n\u001b[1;32m    340\u001b[0m                             _pair(0), self.dilation, self.groups)\n\u001b[1;32m    341\u001b[0m         return F.conv2d(input, weight, self.bias, self.stride,\n\u001b[0;32m--> 342\u001b[0;31m                         self.padding, self.dilation, self.groups)\n\u001b[0m\u001b[1;32m    343\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    344\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "learn.fit_one_cycle(20, max_lr = 1e-4, callbacks=[callbacks.SaveModelCallback(learn, monitor = 'accuracy', mode = 'max')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save('resnet34_cifar_bs64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x720 with 36 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = cnn_learner(data, models.resnet34, metrics = accuracy, pretrained = True)\n",
    "learn.load('resnet34_cifar_bs64')\n",
    "learn.show_results(ds_type=DatasetType.Valid, rows=6, figsize=(8,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (pyt)",
   "language": "python",
   "name": "pyt"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
